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Journal articles on the topic 'Disaggregation in data centers'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Moralis-Pegios, Miltiadis, Nikolaos Terzenidis, George Mourgias-Alexandris, and Konstantinos Vyrsokinos. "Silicon Photonics towards Disaggregation of Resources in Data Centers." Applied Sciences 8, no. 1 (2018): 83. http://dx.doi.org/10.3390/app8010083.

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Coughlin, Tom. "Data Center Disaggregation Using NVMe." IEEE Consumer Electronics Magazine 8, no. 5 (2019): 54–56. http://dx.doi.org/10.1109/mce.2019.2923932.

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Suzuki, Jun, Yoichi Hidaka, Junichi Higuchi, Yuki Hayashi, Masaki Kan, and Takashi Yoshikawa. "Disaggregation and Sharing of I/O Devices in Cloud Data Centers." IEEE Transactions on Computers 65, no. 10 (2016): 3013–26. http://dx.doi.org/10.1109/tc.2015.2513759.

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Zhang, Yiying. "Make It Real: An End-to-End Implementation of A Physically Disaggregated Data Center." ACM SIGOPS Operating Systems Review 57, no. 1 (2023): 1–9. http://dx.doi.org/10.1145/3606557.3606559.

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Resource disaggregation is an approach to separate different hardware resources into independent pools in a data center, so that these pools can be easily managed and their resources can be allocated in a tight but unbounded way. The past decade has seen research and practices in realizing the resource-disaggregation idea on regular servers. We advocate for a physically disaggregated data center, where disaggregated resource pools consist of hardware devices, not servers. Physical disaggregation could unlock another level of benefits in resource disaggregation, including further improved cost

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Zhang, Qizhen, Yifan Cai, Xinyi Chen, et al. "Understanding the effect of data center resource disaggregation on production DBMSs." Proceedings of the VLDB Endowment 13, no. 9 (2020): 1568–81. http://dx.doi.org/10.14778/3397230.3397249.

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Zhao, Matthew Y., Helen Y. Xu, Vidhi Singh, Megan R. Aaronson, Jayraan Badiee, and Folasade (Fola) P. May. "Mo1019: DISAGGREGATION OF RACE AND ETHNICITY DATA TO EXAMINE COLORECTAL CANCER SCREENING RATES AT FEDERALLY QUALIFIED HEALTH CENTERS." Gastroenterology 169, no. 1 (2025): S—981—S—982. https://doi.org/10.1016/s0016-5085(25)03180-4.

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Pagès, Albert, Rubén Serrano, Jordi Perelló, and Salvatore Spadaro. "On the benefits of resource disaggregation for virtual data centre provisioning in optical data centres." Computer Communications 107 (July 2017): 60–74. http://dx.doi.org/10.1016/j.comcom.2017.03.009.

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Rajan, Sanju, and Linda Joseph. "An Adaptable Optimal Network Topology Model for Efficient Data Centre Design in Storage Area Networks." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 2s (2023): 43–50. http://dx.doi.org/10.17762/ijritcc.v11i2s.6027.

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In this research, we look at how different network topologies affect the energy consumption of modular data centre (DC) setups. We use a combined-input directed approach to assess the benefits of rack-scale and pod-scale fragmentation across a variety of electrical, optoelectronic, and composite network architectures in comparison to a conventional DC. When the optical transport architecture is implemented and the appropriate resource components are distributed, the findings reveal fragmentation at the layer level is adequate, even compared to a pod-scale DC. Composable DCs can operate at peak

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Mishra, Vaibhawa, Joshua L. Benjamin, and Georgios Zervas. "MONet: heterogeneous Memory over Optical Network for large-scale data center resource disaggregation." Journal of Optical Communications and Networking 13, no. 5 (2021): 126. http://dx.doi.org/10.1364/jocn.419145.

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Curry-Stevens, Ann. "What Matters: Achieving Racial Equity in a Human Service Non-Profit Organization." Advances in Social Sciences Research Journal 9, no. 1 (2022): 307–38. http://dx.doi.org/10.14738/assrj.91.11552.

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Few research studies exist on the efforts and outcomes undertaken by non-profits aiming to integrate racial equity into their organization. This article, through case study research, identifies how Metropolitan Family Service (in Portland, Oregon) has make seven key gains over the last decade: staff diversification, cultural shift in the organization, board development, data systems that support disaggregation, meaningful community partnerships, high client satisfaction, and most important, more equitable service outcomes. In additional to sharing details on these gains, this article identifie

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Pagès, Albert, Fernando Agraz, and Salvatore Spadaro. "On the Complexity of Configuration and Orchestration for Enabling Disaggregated Server Provisioning in Optical Composable Data Centres." Journal of Optical Communications and Networking 14, no. 12 (2022): 998. http://dx.doi.org/10.1364/jocn.471937.

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Due to the limitations of traditional data center (DC) architectures, the concept of infrastructure disaggregation has been proposed. DC resources are separated into multiple blades to be exploited independently. As a result, composable DC (CDC) infrastructures are achieved, enhancing the modularity of resource provisioning. However, disaggregation introduces additional challenges that need to be carefully analyzed. One relates to the potential complexity increase on the orchestration and infrastructure configuration that need to be performed when provisioning resources to support services. Th

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Pang, Xi, and Jianguo Wang. "Understanding the Performance Implications of the Design Principles in Storage-Disaggregated Databases." Proceedings of the ACM on Management of Data 2, no. 3 (2024): 1–26. http://dx.doi.org/10.1145/3654983.

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Storage-compute disaggregation has recently emerged as a novel architecture in modern data centers, particularly in the cloud. By decoupling compute from storage, this new architecture enables independent and elastic scaling of compute and storage resources, potentially increasing resource utilization and reducing overall costs. To best leverage the disaggregated architecture, a new breed of database systems termed storage-disaggregated databases has recently been developed, such as Amazon Aurora, Microsoft Socrates, Google AlloyDB, Alibaba PolarDB, and Huawei Taurus. However, little is known

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Giannoula, Christina, Kailong Huang, Jonathan Tang, et al. "DaeMon: Architectural Support for Efficient Data Movement in Fully Disaggregated Systems." Proceedings of the ACM on Measurement and Analysis of Computing Systems 7, no. 1 (2023): 1–36. http://dx.doi.org/10.1145/3579445.

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Resource disaggregation offers a cost effective solution to resource scaling, utilization, and failure-handling in data centers by physically separating hardware devices in a server. Servers are architected as pools of processor, memory, and storage devices, organized as independent failure-isolated components interconnected by a high-bandwidth network. A critical challenge, however, is the high performance penalty of accessing data from a remote memory module over the network. Addressing this challenge is difficult as disaggregated systems have high runtime variability in network latencies/ba

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Park, Jong-Hyeok, Soyee Choi, Gihwan Oh, and Sang-Won Lee. "SaS." Proceedings of the VLDB Endowment 14, no. 9 (2021): 1481–88. http://dx.doi.org/10.14778/3461535.3461538.

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Every database engine runs on top of an operating system in the host, strictly separated with the storage. This more-than-half-century-old IHDE (In-Host-Database-Engine) architecture, however, reveals its limitations when run on fast flash memory SSDs. In particular, the IO stacks incur significant run-time overhead and also hinder vertical optimizations between database engines and SSDs. In this paper, we envisage a new database architecture, called SaS (SSD as SQL database engine), where a full-blown SQL database engine runs inside SSD, tightly integrated with SSD architecture without interv

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Dong, Siying, Shiva Shankar P, Satadru Pan, et al. "Disaggregating RocksDB: A Production Experience." Proceedings of the ACM on Management of Data 1, no. 2 (2023): 1–24. http://dx.doi.org/10.1145/3589772.

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As in the general industry, there is a trend in Meta's data centers to migrate data from locally attached SSDs to cloud storage. We extended RocksDB [26], a widely used open-source storage engine designed and built for local SSDs, to leverage disaggregated storage. RocksDB's design, such as its data and log files' access patterns, makes an append-only distributed file system a desirable underlying storage. At Meta, we built disaggregated RocksDB using Tectonic File System [35], which so far had mainly been used for our data warehouse and blob storage stacks. We identified that metadata overhea

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Heintzman, John, Dang Dinh, Jennifer A. Lucas, et al. "Answering calls for rigorous health equity research: a cross-sectional study leveraging electronic health records for data disaggregation in Latinos." Family Medicine and Community Health 11, no. 2 (2023): e001972. http://dx.doi.org/10.1136/fmch-2022-001972.

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IntroductionCountry of birth/nativity information may be crucial to understanding health equity in Latino populations and is routinely called for in health services literature assessing cardiovascular disease and risk, but is not thought to co-occur with longitudinal, objective health information such as that found in electronic health records (EHRs).MethodsWe used a multistate network of community health centres to describe the extent to which country of birth is recorded in EHRs in Latinos, and to describe demographic features and cardiovascular risk profiles by country of birth. We compared

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Shiroma, Kristina, and Jacqueline Miller. "REPRESENTATION OF RURAL OLDER ADULTS IN AI HEALTH RESEARCH: A SYSTEMATIC REVIEW." Innovation in Aging 8, Supplement_1 (2024): 258. https://doi.org/10.1093/geroni/igae098.0833.

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Abstract The demographic shift towards an aging population is particularly pronounced in rural areas, where access to healthcare and social services can be limited. Artificial intelligence (AI) has emerged as a promising avenue for transforming healthcare, particularly for older adults. However, concerns exist regarding equitable access and representation, particularly for geographically isolated, marginalized populations in rural areas. This systematic review investigates the existing research on AI interventions designed to support older adults and their healthcare needs. We searched key hea

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Sedano, Fernando, Abel Mizu-Siampale, Laura Duncanson, and Mengyu Liang. "Influence of Charcoal Production on Forest Degradation in Zambia: A Remote Sensing Perspective." Remote Sensing 14, no. 14 (2022): 3352. http://dx.doi.org/10.3390/rs14143352.

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A multitemporal dataset of medium-resolution imagery was used to document a tree cover loss process in three forest reserves of Zambia. This degradation process was attributed to charcoal production with a high degree of certainty, as evidence of kiln scars was found in more than 85% of sites sampled with high-resolution imagery across the study areas. The spatial and temporal pattern of mapped kiln scars exposed an intense and fast-paced degradation process, with kiln densities reaching 2.3 kiln/ha, removal of about 79.3% of the aboveground biomass and reductions of 74.2% of tree cover. The a

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Schotter, Andreas P. J., Maximilian Stallkamp, and Brian C. Pinkham. "MNE Headquarters Disaggregation: The Formation Antecedents of Regional Management Centers." Journal of Management Studies 54, no. 8 (2017): 1144–69. http://dx.doi.org/10.1111/joms.12285.

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Góis, João, Lucas Pereira, and Nuno Nunes. "A Data-Centric Analysis of the Impact of Non-Electric Data on the Performance of Load Disaggregation Algorithms." Sensors 22, no. 18 (2022): 6914. http://dx.doi.org/10.3390/s22186914.

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Recent research on non-intrusive load monitoring, or load disaggregation, suggests that the performance of algorithms can be affected by factors beyond energy data. In particular, by incorporating non-electric data in load disaggregation analysis, such as building and consumer characteristics, the estimation accuracy of consumption data may be improved. However, this association has rarely been explored in the literature. This work proposes a data-centric methodology for measuring the effect of non-electric characteristics on load disaggregation performance. A real-world dataset is considered

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21

MEHROTRA, R., and R. D. SINGH. "Spatial disaggregation of rainfall data." Hydrological Sciences Journal 43, no. 1 (1998): 91–102. http://dx.doi.org/10.1080/02626669809492104.

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22

Vu, Chau Jo, and Lindsay Turner. "Data Disaggregation in Demand Forecasting." Tourism and Hospitality Research 6, no. 1 (2005): 38–52. http://dx.doi.org/10.1057/palgrave.thr.6040043.

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It is assumed in tourism demand forecasting that the disaggregation of data is useful in terms of country of origin and also in terms of purpose of travel (Smith and Toms, 1967; Blackwell, 1970; Martin and Witt, 1989a). The primary disaggregation by country is useful for determining regional forecast flows and the disaggregation by purpose of visit has been considered potentially useful for increasing forecasting accuracy given the flows have different characteristics (Turner, Kulendran and Pergat, 1995; Morley and Sutikno, 1991). It is also possible to disaggregate on the basis of age and gen

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Liaqat, Rehan, and Intisar Ali Sajjad. "An Event Matching Energy Disaggregation Algorithm Using Smart Meter Data." Electronics 11, no. 21 (2022): 3596. http://dx.doi.org/10.3390/electronics11213596.

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Energy disaggregation algorithms disintegrate aggregate demand into appliance-level demands. Among various energy disaggregation approaches, non-intrusive load monitoring (NILM) algorithms requiring a single sensor have gained much attention in recent years. Various machine learning and optimization-based NILM approaches are available in the literature, but bulk training data and high computational time are their respective drawbacks. Considering these drawbacks, we devised an event matching energy disaggregation algorithm (EMEDA) for NILM of multistate household appliances using smart meter d

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Chen, Zhigang, Haotian Peng, and Yongxin Su. "Nonintrusive load disaggregation by fusion of graph signal processing and CNN." Journal of Physics: Conference Series 2853, no. 1 (2024): 012061. http://dx.doi.org/10.1088/1742-6596/2853/1/012061.

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Abstract Nonintrusive load disaggregation techniques play a pivotal role in power system planning and decision making for demand response. The sparsity of low frequency total power data features, coupled with the model’s challenge in effectively extracting and utilizing spatial and temporal correlations in diverse load data, presents obstacles to achieving high precision disaggregation in nonintrusive load disaggregation. To address these challenges, this paper proposes a disaggregation framework that integrates graph signal processing (GSP) with a convolutional neural network (CNN). In this f

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Bouchur, Mazen, and Andreas Reinhardt. "Synergistic Non-Intrusive Load Monitoring: Dual-Model Training and Inference for Improved Load Disaggregation Prediction." Energies 18, no. 3 (2025): 608. https://doi.org/10.3390/en18030608.

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Load disaggregation is the process of identifying an individual appliance’s power demand within aggregate electrical load data. Virtually all recently proposed disaggregation methods are based on neural networks, thanks to their superior performance. Their achievable accuracy is, however, often limited by the quality of the data that have been used to train the underlying neural networks. In particular, if electrical appliances exhibit vastly differing temporal schedules and operational modes, their heterogeneous power consumption data can poison the disaggregation models and thus lead to a de

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Monteiro, Martins, Murrieta-Flores, and Moura Pires. "Spatial Disaggregation of Historical Census Data Leveraging Multiple Sources of Ancillary Information." ISPRS International Journal of Geo-Information 8, no. 8 (2019): 327. http://dx.doi.org/10.3390/ijgi8080327.

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High-resolution population grids built from historical census data can ease the analyses ofgeographical population changes, at the same time also facilitating the combination of populationdata with other GIS layers to perform analyses on a wide range of topics. This article reports onexperiments with a hybrid spatial disaggregation technique that combines the ideas of dasymetricmapping and pycnophylactic interpolation, using modern machine learning methods to combinedifferent types of ancillary variables, in order to disaggregate historical census data into a 200 mresolution grid. We specifica

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Fadhel, Sherien, Mustafa Al Aukidy, and May Samir Saleh. "Uncertainty of Intensity-Duration-Frequency Curves Due to Adoption or Otherwise of the Temperature Climate Variable in Rainfall Disaggregation." Water 13, no. 17 (2021): 2337. http://dx.doi.org/10.3390/w13172337.

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Most areas around the world lack fine rainfall records which are needed to derive Intensity-Duration-Frequency (IDF) curves, and those that are available are in the form of daily data. Thus, the disaggregation of rainfall data from coarse to fine temporal resolution may offer a solution to that problem. Most of the previous studies have adopted only historical rainfall data as the predictor to disaggregate daily rainfall data to hourly resolution, while only a few studies have adopted other historical climate variables besides rainfall for such a purpose. Therefore, this study adopts and asses

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Maheepala, S., and B. J. C. Perera. "Monthly hydrologic data generation by disaggregation." Journal of Hydrology 178, no. 1-4 (1996): 277–91. http://dx.doi.org/10.1016/0022-1694(95)02834-x.

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Du, Zhekai, Jingjing Li, Lei Zhu, Ke Lu, and Heng Tao Shen. "Adversarial Energy Disaggregation." ACM/IMS Transactions on Data Science 2, no. 4 (2021): 1–16. http://dx.doi.org/10.1145/3477301.

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Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual consumptions, which is a typical application of data analysis. NILM aims to help households understand how the energy is used and consequently tell them how to effectively manage the energy, thus allowing energy efficiency, which is considered as one of the twin pillars of sustainable energy policy (i.e., energy efficiency and renewable energy). Although NILM is unidentifiable, it is widely believed that the NILM pro

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Libby, Robert, and Timothy Brown. "Financial Statement Disaggregation Decisions and Auditors' Tolerance for Misstatement." Accounting Review 88, no. 2 (2012): 641–65. http://dx.doi.org/10.2308/accr-50332.

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ABSTRACT Current IFRS requires significant disaggregation of income statement numbers while such disaggregation is voluntary and much less common under U.S. GAAP. We examine whether voluntary disaggregation of income statement numbers increases the reliability of income statement subtotals because auditors permit less misstatement in the disaggregated numbers. In our experiment, experienced auditors require correction of smaller errors in disaggregated numbers. Auditors also believe that greater disaggregation will increase SEC scrutiny of uncorrected financial statement errors in the disaggre

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Góis, João, and Lucas Pereira. "Appliance-Specific Noise-Aware Hyperparameter Tuning for Enhancing Non-Intrusive Load Monitoring Systems." Energies 18, no. 14 (2025): 3847. https://doi.org/10.3390/en18143847.

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Load disaggregation has emerged as an effective tool for enabling smarter energy management in residential and commercial buildings. By providing appliance-level energy consumption estimation from aggregate data, it supports energy efficiency initiatives, demand-side management, and user awareness. However, several challenges remain in improving the accuracy of energy disaggregation methods. For instance, the amount of noise in energy consumption datasets can heavily impact the accuracy of disaggregation algorithms, especially for low-power consumption appliances. While disaggregation performa

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Gafurov, Marat, Yulia Ganeeva, Tatyana Yusupova, Fadis Murzakhanov, and Georgy Mamin. "High-Field (3.4 T) Electron Paramagnetic Resonance, 1H Electron-Nuclear Double Resonance, ESEEM, HYSCORE, and Relaxation Studies of Asphaltene Solubility Fractions of Bitumen for Structural Characterization of Intrinsic Carbon-Centered Radicals." Nanomaterials 12, no. 23 (2022): 4218. http://dx.doi.org/10.3390/nano12234218.

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Petroleum asphaltenes are considered the most irritating components of various oil systems, complicating the extraction, transportation, and processing of hydrocarbons. Despite the fact that the paramagnetic properties of asphaltenes and their aggregates have been studied since the 1950s, there is still no clear understanding of the structure of stable paramagnetic centers in petroleum systems. The paper considers the possibilities of various electron paramagnetic resonance (EPR) techniques to study petroleum asphaltenes and their solubility fractions using a carbon-centered stable free radica

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Monteiro, João, Bruno Martins, Miguel Costa, and João M. Pires. "Geospatial Data Disaggregation through Self-Trained Encoder–Decoder Convolutional Models." ISPRS International Journal of Geo-Information 10, no. 9 (2021): 619. http://dx.doi.org/10.3390/ijgi10090619.

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Datasets collecting demographic and socio-economic statistics are widely available. Still, the data are often only released for highly aggregated geospatial areas, which can mask important local hotspots. When conducting spatial analysis, one often needs to disaggregate the source data, transforming the statistics reported for a set of source zones into values for a set of target zones, with a different geometry and a higher spatial resolution. This article reports on a novel dasymetric disaggregation method that uses encoder–decoder convolutional neural networks, similar to those adopted in i

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Roegman, Rachel, Ala Samarapungavan, Yukiko Maeda, and Gary Johns. "Color-Neutral Disaggregation? Principals’ Practices Around Disaggregating Data From Three School Districts." Educational Administration Quarterly 54, no. 4 (2018): 559–88. http://dx.doi.org/10.1177/0013161x18769052.

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Purpose: We explored the practices and understandings around using disaggregated data to inform instruction of 18 principals from three Midwestern school districts. Research Method: This qualitative study used one-on-one semistructured interviews with the principals focusing on how they disaggregate data in practice. The protocol included general questions about principals’ data practices as well as specific questions around disaggregation. Initial inductive coding began with principals’ direct responses to specific questions around disaggregation, and then emerging themes were used to analyze

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Oyetayo, Oluwatosin Juliana, Ebenezer Adesoji Olubiyi, and Mathew Adekunle Abioro. "Households’ Population Data Disaggregation: A Financial Perspective." Indian Journal of Economics and Development 9 (December 20, 2021): 1–10. http://dx.doi.org/10.17485/ijed/v9.2021.53.

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De La Cruz-Viesca, Melany. "Disaggregation Matters: Asian Americans and Wealth Data." AAPI Nexus Journal: Policy, Practice, and Community 9, no. 1-2 (2011): 91–100. http://dx.doi.org/10.36650/nexus9.1-2_91-100_delacruz-viesca.

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This policy brief explores the usefulness and limitations of existing federal government data sets in better understanding the wealth position and asset-building needs of Asian Americans. As Asian Americans continue to be one of the fastest-growing racial groups in the United States, it is critical for federal data sets to disaggregate Asian Americans by ethnicity and by immigrant versus nonimmigrant status, in order to provide a more accurate and nuanced analysis of the Asian American experience with asset accumulation. The lumping of all Asian American ethnic groups under the aggregate “Asia

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Kauh, Tina J., Terry Ao Minnis, Meeta Anand, Maya Berry, and Rosalind Gold. "Building an Equitable Future Through Data Disaggregation." Health Equity 7, no. 1 (2023): 251–60. http://dx.doi.org/10.1089/heq.2023.29036.rtd.

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Huynh, Grace, Sarah Fleischer, and Lars E. Peterson. "Data Disaggregation of Asian-American Family Physicians." Journal of the American Board of Family Medicine 37, no. 2 (2024): 349–50. http://dx.doi.org/10.3122/jabfm.2023.230326r1.

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Gafarova, Elena A. "The estimation of gross regional product leading indicator by temporal dissaggregation method." Вестник Пермского университета Серия «Экономика» = Perm University Herald ECONOMY 19, no. 3 (2024): 253–68. http://dx.doi.org/10.17072/1994-9960-2024-3-253-268.

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Introduction. Тhe estimation of gross regional product high-frequency leading indicator is relevant for the reliable analysis of current trends in regional economy and early understanding of its changes in the periods of high uncertainty since gross regional product is published on an annual basis. One approach to receive this indicator is a temporal disaggregation method, which has proven to be reasonable in foreign literature for disaggregating gross domestic product. At the same time, temporal disaggregation of regional economic time series has been understudied. Purpose. The purpose of the

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Gebbe, Christian, Fabian Klemm, Simon Zhai, and Gunther Reinhart. "Estimating Machine Power Consumptions through Aggregated Measurements and Machine Data Acquisition." Applied Mechanics and Materials 655 (October 2014): 61–66. http://dx.doi.org/10.4028/www.scientific.net/amm.655.61.

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Monitoring the electric power consumption of machines in manufacturing becomes more and more important due to rising electricity costs and environmental awareness. This paper proposes a cost-efficient monitoring concept requiring significantly fewer electricity meters. Power consumption of several machines is monitored aggregately and then broken down into the estimated power consumption of each machine by means of a disaggregation procedure utilizing machine data acquisition. In this paper the disaggregation procedure is presented, uncertainty limitations are discussed and an application exam

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Becker, Tara, Susan H. Babey, Rashida Dorsey, and Ninez A. Ponce. "Data Disaggregation with American Indian/Alaska Native Population Data." Population Research and Policy Review 40, no. 1 (2021): 103–25. http://dx.doi.org/10.1007/s11113-020-09635-2.

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Al-Zakar, Shatha H. D., N. Şarlak, and Omar M. A. Mahmood Agha. "Disaggregation of Annual to Monthly Streamflow: A Case Study of Kızılırmak Basin (Turkey)." Advances in Meteorology 2017 (2017): 1–16. http://dx.doi.org/10.1155/2017/3582826.

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This study utilizes a recent nonparametric disaggregation K-nearest neighbor (KNN) model, to resample monthly flows depending on annual flows at different sites. Both temporal and spatial approaches will be followed in this model while preserving the distributional statistics of the observed data. This model assumes that a set of aggregated annual streamflows at a key station is available and desired for disaggregation to a corresponding series of streamflow at key station (temporal disaggregation) as well as at tributary stations (spatial disaggregation). The model is applied to the annual st

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Mauro, Anna Di, Armando Di Nardo, Giovanni Francesco Santonastaso, and Salvatore Venticinque. "Development of an IoT System for the Generation of a Database of Residential Water End-Use Consumption Time Series." Environmental Sciences Proceedings 2, no. 1 (2020): 20. http://dx.doi.org/10.3390/environsciproc2020002020.

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Disaggregation techniques are useful to separate data aggregated into single end-use categories, and they are becoming of great interest in the water sector due to the technological innovation in metering systems that has made water consumption data available. However, in order to apply disaggregation methods, high-resolution data at the end-use level are needed. To face this problem, the paper presents an Internet of Things water end-use monitoring system that is able to read real-time end-use consumption in a residential apartment equipped as a pilot site. Moreover, the paper describes preli

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P.J. Kleynhans, Ewert, and Clive Egbert Coetzee. "Temporal Disaggregation of Gross Domestic Product Data with Night-Time Lights Data for South Africa." African Journal of Business and Economic Research 17, no. 4 (2022): 233–59. http://dx.doi.org/10.31920/1750-4562/2022/v17n4a11.

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The absence of high-frequency time series data is a real constraint for many researchers worldwide. Generating high-frequency economic data, especially at the sub-national level, generates significant advantages and opportunities. Research otherwise impossible now becomes possible and indeed desirable. Therefore, this study aims to investigate the usefulness of several temporal disaggregation methods and the reliability of the derived results. Temporal disaggregation is one such method that can be used to generate high-frequency time series data from low-frequency time series data. The methods

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Marciniak, Stanley, Mark Furman, Alan Michelson, et al. "An Additional Mechanism of Action of Abciximab: Dispersal of Newly Formed Platelet Aggregates." Thrombosis and Haemostasis 87, no. 06 (2002): 1020–25. http://dx.doi.org/10.1055/s-0037-1613127.

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SummaryThe ability of abciximab to prevent fibrinogen binding to activated platelets indicates it may also promote dissolution of platelet-rich thrombi. The present study examined the capacity of abciximab to reverse platelet aggregation in vitro. Experiments were performed on blood from healthy non-medicated donors. Platelet aggregate formation and disaggregation were monitored turbidimetrically. Platelet-bound fibrinogen was measured by flow cytometry. For disaggregation studies, platelets were first stimulated with either ADP or the 11-mer thrombin receptor activating peptide (TRAP), then v

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Gawin, Bartłomiej, Robert Małkowski, and Robert Rink. "Will NILM Technology Replace Multi-Meter Telemetry Systems for Monitoring Electricity Consumption?" Energies 16, no. 5 (2023): 2275. http://dx.doi.org/10.3390/en16052275.

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The estimation of electric power utilization, its baseload, and its heating, light, ventilation, and air-conditioning (HVAC) power component, which represents a very large portion of electricity usage in commercial facilities, are important for energy consumption controls and planning. Non-intrusive load monitoring (NILM) is the analytical method used to monitor the energy and disaggregate total electrical usage into appliance-related signals as an alternative to installing multiple electricity meters in the building. However, despite considerable progress, there are a limited number of tools

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Pallegedara Dewage, Sanjeewani Nimalka Somarathna, Budiman Minasny, and Brendan Malone. "Disaggregating a regional-extent digital soil map using Bayesian area-to-point regression kriging for farm-scale soil carbon assessment." SOIL 6, no. 2 (2020): 359–69. http://dx.doi.org/10.5194/soil-6-359-2020.

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Abstract. Most soil management activities are implemented at farm scale, yet digital soil maps are commonly available at regional or national scale. Disaggregating these regional and/or national maps is applicable for farm-scale tasks, particularly in data-poor or limited situations. Although disaggregation is a frequently discussed topic in recent digital soil mapping literature, the uncertainty of the disaggregation process is not often discussed. Underestimation of inferential or predictive uncertainty in statistical modelling leads to inaccurate statistical summaries and overconfident deci

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Park, Seo Jin, Ramesh Govindan, Kai Shen, et al. "Lovelock: Towards Smart NIC-Hosted Clusters." ACM SIGEnergy Energy Informatics Review 4, no. 5 (2024): 172–79. https://doi.org/10.1145/3727200.3727226.

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Traditional cluster designs were originally server-centric, and have evolved recently to support hardware acceleration and storage disaggregation. In applications that leverage acceleration, the server CPU performs the role of orchestrating computation and data movement and data-intensive applications stress the memory bandwidth. Applications that leverage disaggregation can be adversely affected by the increased PCIe and network bandwidth resulting from disaggregation. In this paper, we advocate for a specialized cluster design for important data intensive applications, such as analytics, que

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Casares, Serafín Ojeda, Joaquín Valverde Martínez, Ana Ramírez Torres, and Iria Enrique Regueira. "The statistical grid as a unit of observation and analysis: Andalusia, Spanish region case study." Investigaciones Regionales - Journal of Regional Research 59 (April 17, 2024): 149–65. http://dx.doi.org/10.38191/iirr-jorr.24.015.

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Units of observation with reduced dimensions and regular geometry have been increasingly generated in a recent, albeit already steady, trend towards further territorial data disaggregation. In this line of research, the present study reports the results of using the spatial distribution of population data and built-up areas at a high level of territorial data disaggregation with reduced dimensions and with a homogeneous observation unit by applying a regular grid consisting of 250-m square cells. e main objective was to show the results and advantages of working at a high level of spatial dat

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Ojha, Nitu, Olivier Merlin, Beatriz Molero, et al. "Stepwise Disaggregation of SMAP Soil Moisture at 100 m Resolution Using Landsat-7/8 Data and a Varying Intermediate Resolution." Remote Sensing 11, no. 16 (2019): 1863. http://dx.doi.org/10.3390/rs11161863.

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Global soil moisture (SM) products are currently available from passive microwave sensors at typically 40 km spatial resolution. Although recent efforts have been made to produce 1 km resolution data from the disaggregation of coarse scale observations, the targeted resolution of available SM data is still far from the requirements of fine-scale hydrological and agricultural studies. To fill the gap, a new disaggregation scheme of Soil Moisture Active and Passive (SMAP) data is proposed at 100 m resolution by using the disaggregation based on physical and theoretical scale change (DISPATCH) al

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